AKRASIA: Stealthy Backdoor Attack on Reasoning-based Code LLMs

2026-09-01

AI Summary

本文提出AKRASIA，一种针对基于推理的代码语言模型的隐秘后门攻击方法，通过构建代码级触发器和利用上下文学习来实现恶意目标并规避检测。

Abstract

We present AKRASIA, a stealthy, inference-time backdoor attack against reasoning-based Code LLMs. AKRASIA aims to achieve a backdoor target (e.g., malicious code execution) in reasoning LLMs while evading automated defenses and human inspection. To achieve this, AKRASIA probes the victim LLM to construct a code-level backdoor trigger. It then employs in-context learning for backdoor learning, and model unfaithfulness to conceal the backdoor trigger, and generate plausible reasoning. We evaluate AKRASIA using four backdoor targets six (6) reasoning LLMs, three coding tasks/datasets and three defense methods. AKRASIA has up to 99.34% average attack success rate on SOTA LLMs and mantains up to 97.23% average accuracy. AKRASIA evades the SOTA defense, retaining up to 98.82% average ASR in most (14/18) defense settings. It evades human inspection, successfully hiding the backdoor trigger and reasoning steps in up to 80% of settings. Our findings motivate the need to defend LLMs against reasoning backdoors.

Problem

• backdoor attack
• reasoning-based Code LLMs
• inference-time
• stealthy

Innovation

• stealthy backdoor attack
• reasoning-based LLMs
• in-context learning
• model unfaithfulness
• evading defenses